Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification
Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification
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DOI：
10.3390/ijgi9050329
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发表时间：
2020-05-01
影响因子：
3.4
通讯作者：
Ranagalage, Manjula
中科院分区：
文献类型：
--
作者：
Phiri, Darius;Simwanda, Matamyo;Ranagalage, Manjula
决策树（DT）算法是用于土地覆盖分类的重要非参数工具。虽然对大地遥感卫星土地覆盖分类应用了不同的DT，但尚未对它们各自的分类准确性和性能进行比较，特别是在它们为制定基于物体的土地覆盖分类规则集而产生准确阈值的有效性方面。在这里，重点是比较五种DT算法的性能：Tree，C5.0，Rpart，Ipred和Party。这些DT算法被用来分类10个土地覆盖类，使用Landsat 8图像上的铜带省的赞比亚。通过开发具有DT定义的阈值的规则集，使用基于对象的图像分析（OBIA）进行分类。DT算法的性能进行了评估的基础上：（1）DT精度通过交叉验证;（2）专题地图的土地覆盖分类精度;（3）其他结构属性，如树图的大小和变量选择能力。结果表明，只有DT算法开发的规则集具有简单的结构和最小的变量数产生高的土地覆盖分类精度（总精度> 88%）。因此，与分类中涉及许多变量的C5.0和PartyDT算法相比，诸如Tree和Rpart的算法产生更高的分类结果。这种高精度归因于最小化过拟合的能力以及在树和Rpart DT训练期间处理数据中噪声的能力。这项研究产生了新的见解正式选择DT算法OBIA规则的发展。因此，树和Rpart算法可以用于开发规则集，因为它们产生高的土地覆盖分类精度和具有简单的结构。作为未来研究的一种途径，DT算法的性能可以与当代机器学习分类器（例如，随机森林和支持向量机）。
Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine).